Data is, arguably, the world’s most precious resource and perhaps the most omnipresent and omnipotent one. Since the devil lies in its details (as also our deliverance), we need to make sense of this data deluge to direct actionable analysis and effective decision-making.

With environmental, social and governance (ESG) activities playing an increasingly important role in asset management, accurate, consistent and trustworthy ESG data has become essential not only for downstream reporting and decision-making but also for meeting sustainability goals.

To determine if the data your firm handles fulfils these parameters, ask yourself this - how are we integrating ESG factors into our investment decision-making processes and what steps are we taking to ensure we are complying with evolving regulations?

How should you, as an asset manager (AM), source, ingest and process ESG data?

Building on our previous blog on the overall ESG data management strategy¹, this blog dives deeper into the first stage of the ESG lifecycle - handling complex and ever-changing regulations, and then turning those regulations into tangible data attributes for the business, risk and compliance teams. 

Let’s start with how to approach ESG data ingestion and aggregation, from key drivers to implementing an enhanced data ingestion process.

Step 1: Define the drivers for ESG data ingestion

Grappling as you are with a plethora of challenges when managing and interpreting volumes of data in a dynamic asset management landscape, it is important to determine the key drivers for enhancing ESG data ingestion. 

• Overabundance of vendors: There are a multitude of ESG data vendors, but standardisation across vendors is missing, with differing data formats, definitions and categories 
• Incomplete coverage: Data vendors are not required to abide by regulatory standards, requiring AMs to develop comprehensive data taxonomies and metadata management or risk non-compliance and fines
• Lack of data infrastructure: Many AMs do not have an architecture for a robust ESG data pipeline in place that can unite disparate sources, thus resulting in data quality issues
• Inability to derive insights: ESG data has the potential to drive strategic decision-making, but AMs are largely leveraging it only to satisfy compliance requirements, wasting valuable monetization potential
• Lack of automation: Sourcing and validation of data is still done manually, causing a significant time and effort bottleneck in delivery of meaningful insights, amplified by limited scalability and lack of auditability

Once you have identified the drivers, you will need to obtain stakeholder buy-in for why they are important to enhancing current ESG initiatives.

Step 2: Identify key ESG data sources

Now that you understand and agree on why you need enhancements, what’s next? Let’s look at the data you are currently receiving. Regulatory bodies have been increasingly implementing and refining standards, such as Sustainable Finance Disclosure Regulation (SFDR), Task Force on Climate-Related Financial Disclosures (TCFD), International Sustainability Standards Board and the Securities and Exchange Commission’s Enhancement and Standardisation of Climate-Related Disclosures for Investors, to guide and enforce sustainable finance. As a result, AMs are reconsidering appropriate data strategies to identify the best ESG data sources and handle ingestion and aggregation efficiently for the exponentially increased volume and variety of data. 

Data is being captured through various approaches such as: 

• Using a rules-based methodology to identify industry leaders and laggards based on their exposure to ESG risks and how well they manage those risks relative to peers
• Identifying financially material ESG risks at the security and portfolio levels by capturing exposure to industry-specific ESG risks and management of those risks
• Leveraging an overall rating method that breaks down into underlying pillar and theme exposures and scores; the pillars and themes are built on multiple individual indicator assessments
• Publishing a variety of scores including corporate ESG-related risks, opportunities and impacts along the operational value chain
• Using a relative score to measure a company’s performance on and management of ESG risks, opportunities and impacts compared with its peers within the same industry classification
  

Considering these will help your firm establish additional credibility with investors and regulators alike.

Step 3: Apply ESG data ingestion and aggregation best practices

Once you have your target data sources identified, it’s time to ensure you are applying industry best practices to your workflow design. This should be defined as part of your ESG data strategy, ensuring your firm follows industry guidelines, recognizes nuances of the ESG function and factors in available technology. You can consider some of these key best practices:

• Concentrate on target datasets: Sift through and isolate a small sample of datasets that will be subject to analysis and disambiguation to form the foundations for your ESG data model
• Evaluate key data inputs: Conduct a holistic assessment of all the data vendors, sources, standards and tools (current/ target); then, determine the best operating model for seamless data flow
• Consolidate fields and definitions: Define a comprehensive ESG data dictionary and taxonomy based on the overlap in attributes across the target datasets and standardize all incoming data accordingly
• Centralize storage location: Transfer all incoming ESG data to one unified location for preprocessing, cleansing and standardization, constructing an environment where rules and parameters can be adjusted
• Create nested logic structures: Enhance basic Boolean operations with multi-level triggers and criteria, outlining multiple conditions to aggregate basic attributes into advanced ESG variables
• Prioritize crucial controls: Designate appropriate control responses tagged to critical internal ESG processes based on the amount of testing and auditing done to delineate a corresponding hierarchy
• Design API-based data intake: Utilize functions that can be easily called to automate data collection, facilitate straightforward workflows and create a centralized repository for customization
  

Step 4: Design and implement your ESG data ingestion and aggregation workflow

Once you have completed these steps, the true fun begins - implementing sound ESG data ingestion and aggregation models. The below illustrates how to implement a pipeline for ingestion and aggregation:

• Data acquisition: Establish inbound data streams with relevant data vendors and datasets and create a complete mapping of dataset to data sources
• Validation and ingestion: Set gated checks to ensure there are no mismatches or errors in acquisition pathways, and ensure that datasets are complete, non-duplicative and accurate
• Data cleansing: Optimize data quality based on assessment dimensions, and then remove or remediate blanks and error values and sanitize any extraneous information deemed unnecessary for the workflow
• Data rules and standardization: Apply unified rules based on internal taxonomy to transform data into digestible formats. This includes metadata management activities such as aligning field attributes, semantics and definitions across datasets
• Classification and categorization: Utilize sorting logic to group data points based on use case, format, function and any other metadata attributes relevant to the specific workflow
• Centralization and storage: Move the transformed and prepared data into a dedicated warehousing location that is in the cloud or on-premises, developing an internal organizational structure for straightforward data access
• Extraction and transformation: Pull pertinent data based on targeted downstream use case and apply transformation rules based on the desired format and output of the data
• Downstream use cases: Deliver data to dependent workflows, ensure continuous integration and enable live data streaming, where upstream changes in the ingestion process are reflected in parallel
  

ESG data ingestion in practice

Now that we’ve walked through the steps, let’s take a look at a live case study of establishing an enterprise-wide ESG data taxonomy and ingestion platform.

Conclusion

If your financial institution is to meet the requirements of its investors and regulators, the first step is to ensure data is ingested from the correct sources, metadata is mapped correctly and data pipelines are designed in collaboration with the business.

Effective and systemic ingestion and aggregation of ESG data will not only empower your firm to navigate the complexities of sustainability reporting, but also position it as a responsible steward, fostering transparency, accountability and informed decision-making for a more sustainable future.

In the next blog of our ESG data management series, we will look at the appropriate technology and platforms for handling the storage of ESG data. 

¹ Unlock value via ESG data management strategies by CRISIL GR&RS